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#!/bin/env python3
import sys
import torchaudio
import torchaudio.transforms as T
import torch
import torch.nn as nn
import torch.optim as optim
import torch.utils.data as data
import torch.nn.functional as F
CHARS = "ABCDEFGHIJKLMNOPQRSTUVWXYZ' "
EPOCHS = 25
FEATURES = 13 # idk
print("getting datasets...")
# download the datasets
train_dataset = torchaudio.datasets.LIBRISPEECH(
"./data", url="train-clean-100", download=True
)
test_dataset = torchaudio.datasets.LIBRISPEECH(
"./data", url="test-clean", download=True
)
print("got datasets!")
def preprocess(data):
transform = T.MFCC(sample_rate=16000, n_mfcc=FEATURES)
inputs = []
targets = []
input_lengths = []
target_lengths = []
for audio in data:
input = transform(audio[0]).squeeze(0).transpose(0, 1)
inputs.append(input)
target = torch.Tensor([CHARS.index(c) for c in audio[2]])
targets.append(target)
input_lengths.append(input.shape[0])
target_lengths.append(len(target))
inputs = nn.utils.rnn.pad_sequence(inputs, batch_first=True)
targets = nn.utils.rnn.pad_sequence(targets, batch_first=True)
return (inputs, targets, input_lengths, target_lengths)
print("preprocessing datasets...")
# load the datasets into batches
train_loader = data.DataLoader(
train_dataset, batch_size=10, shuffle=True, collate_fn=preprocess
)
test_loader = data.DataLoader(
test_dataset, batch_size=10, shuffle=True, collate_fn=preprocess
)
print("datasets ready!")
class Model(nn.Module):
def __init__(self, input_size, hidden_size, output_size, num_layers=1):
super().__init__()
self.lstm = nn.LSTM(
input_size, hidden_size, num_layers, batch_first=True
)
self.fc = nn.Linear(hidden_size, output_size)
def forward(self, x):
x, _ = self.lstm(x)
x = self.fc(x)
return x
model = Model(input_size=FEATURES, hidden_size=128, output_size=len(CHARS) + 1)
def train():
print("training model...")
criterion = nn.CTCLoss(blank=len(CHARS))
optimizer = optim.Adam(model.parameters())
for epoch in range(EPOCHS):
for i, (inputs, targets, input_lengths, target_lengths) in enumerate(
train_loader
):
print(f"epoch {epoch}/{EPOCHS}, iteration {i}/{len(train_loader)}")
optimizer.zero_grad()
outputs = model(inputs)
outputs = F.log_softmax(outputs, dim=2)
outputs = outputs.transpose(0, 1)
loss = criterion(outputs, targets, input_lengths, target_lengths)
loss.backward()
optimizer.step()
torch.save(model.state_dict(), f"target/model-{epoch}.ckpt")
torch.save(model.state_dict(), "target/model-final.ckpt")
print("model trained!")
def ctc_decode(outputs, labels, label_lengths):
arg_maxes = torch.argmax(outputs, dim=2)
inferred = ""
target = ""
for i, args in enumerate(arg_maxes):
target += "".join(
[CHARS[int(c)] for c in labels[i][: label_lengths[i]].tolist()]
)
decode = []
for j, ind in enumerate(args):
if ind != len(CHARS):
if j != 0 and ind == args[j - 1]:
continue
decode.append(ind.item())
inferred += "".join([CHARS[c] for c in decode])
return inferred, target
def test():
model.load_state_dict(torch.load("target/model-final.ckpt"))
# TODO: Calculate accuracy using string difference functions
with torch.no_grad():
for i, (inputs, targets, input_lengths, target_lengths) in enumerate(
test_loader
):
outputs = model(inputs)
inferred, target = ctc_decode(outputs, targets, target_lengths)
print("\n=========================================\n")
print("inferred: ", inferred)
print("")
print("target: ", target)
def usage():
print(f"usage: {sys.argv[0]} <train|test>")
if __name__ == "__main__":
if len(sys.argv) != 2:
usage()
sys.exit(1)
if sys.argv[1] == "train":
train()
elif sys.argv[1] == "test":
test()
else:
usage()
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